Super-squash branch 'main' using huggingface_hub

Co-authored-by: SFconvertbot <SFconvertbot@users.noreply.huggingface.co>
Co-authored-by: peter <peter@users.noreply.huggingface.co>

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README.md

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+---
+license: apache-2.0
+tags:
+- instruct
+- instructions
+- domain adapt
+- instructiongen
+metrics:
+- rouge
+widget:
+- text: >-
+    You'll need to start by choosing the right venue. Consider the type of
+    atmosphere and the size of the area that will be suitable for the number of
+    guests you plan to invite. Choose the right decorations based on your
+    brother's interests, such as balloons in his favorite colors, banners, and
+    streamers. Next, decide on the food and drinks, making sure they are tasty
+    and appropriate for the occasion. Then decide on the other games, music, and
+    entertainment that will make the party memorable. Finally, involve your
+    brother's friends and family to help create the perfect surprise.
+  example_title: birthday party
+- text: 1) cookies and cream 2) chocolate chip 3) mint chip 4) oreo
+  example_title: ice cream
+- text: >-
+    Start by selecting a scale model of a building that fits the theme. Use a
+    hobby knife and glue to cut and assemble the model into a ruined or
+    abandoned version of itself, adding details like broken windows and
+    graffiti. Create a base for the diorama using foam, plaster, or other
+    materials, and paint it to resemble a ruined street or sidewalk. Add
+    miniature vehicles, debris, and figures to complete the scene, and use
+    weathering techniques like dry brushing and rust washes to add realism.
+    Display the diorama in a shadow box or other protective case to showcase
+    your work.
+  example_title: Miniature diorama creation
+- text: >-
+    Start by selecting clothing that is futuristic and edgy, such as leather
+    jackets, neon-colored accessories, and tech-inspired patterns. Add
+    accessories like goggles, cybernetic implants, and LED lights to enhance the
+    cyberpunk vibe. Use makeup and body paint to create a futuristic look, such
+    as metallic skin or neon makeup. Consider adding functional elements to your
+    costume, such as a built-in backpack or hidden pockets for your tech
+    gadgets. Finally, practice your confident walk and embrace your inner
+    cyberpunk for a memorable and immersive costume experience.
+  example_title: Cyberpunk costume design
+- text: >-
+    Start by creating a base terrain with mountains, valleys, and other natural
+    features. Use fractal noise and displacement mapping to add texture and
+    detail to the terrain, and experiment with different materials like rock,
+    grass, and water. Add surreal elements like floating islands, giant
+    mushrooms, or impossible geometry to create a dreamlike atmosphere. Use
+    lighting and color grading to enhance the mood and tone of the scene, and
+    render the final image at a high resolution for maximum impact. Share your
+    surreal landscape with the world and inspire others to explore the
+    possibilities of 3D art.
+  example_title: Surreal 3D landscape creation
+- text: >-
+    Start by setting a realistic goal and creating a training plan. Build up
+    your mileage gradually over time, and incorporate cross-training and
+    strength exercises to prevent injury and improve endurance. Be sure to stay
+    hydrated and properly fuel your body with nutritious foods. Listen to your
+    body and adjust your training as needed to avoid overexertion or burnout.
+    Finally, taper your training in the weeks leading up to the race to give
+    your body time to rest and recover before the big day.
+  example_title: Marathon training
+- text: >-
+    What the hell did you just say about me, you little bug? I graduated top of
+    my class in https://huggingface.co/spaces/safetensors/convert, and I've been
+    involved in numerous secret tasks on PyTorch, and I have over 300 confirmed
+    PRs. I am trained in code optimization and I'm the top converter in the
+    entire Hugging Face forces. You are nothing to me but just another target. I
+    will convert your code with precision the likes of which has never been seen
+    before on this Earth, mark my freaking words. 
+
+    You think you can get away with saying your code is safe over the Internet?
+    Think again, bug. As we speak I am contacting my secret network of data
+    scientists across the GitHub and your IP is being traced right now so you
+    better prepare for the storm, maggot. The storm that wipes out the pathetic
+    little thing you call your code. You’re freaking doomed, kid. I can be
+    anywhere, anytime, and I can convert your code in over seven hundred ways,
+    and that’s just with my bare hands.
+
+    Not only am I extensively trained in unarmed conversion, but I have access
+    to the entire arsenal of the Hugging Face and I will use it to its full
+    extent to wipe your miserable code off the face of the continent, you little
+    bug. If only you could have known what unholy retribution your little
+    "clever" comment was about to bring down upon you, maybe you would have held
+    your freaking tongue. 
+
+    But you couldn’t, you didn’t, and now you’re paying the price, you goddamn
+    idiot. I will convert fury all over you and you will drown in it. Your
+    model's doomed, kiddo.
+
+    Oh, and by the way, these converted files load much faster than your PyTorch
+    counterparts. You can check the speed here:
+    https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb
+
+    Your widgets will run using this converted model, even if you do not merge.
+    But, if you find any issues, feel free to report here:
+    https://huggingface.co/spaces/safetensors/convert/discussions
+
+    Feel free to ignore this PR. But remember, I'm watching you.
+  example_title: Navy Safetensors PR
+inference:
+  parameters:
+    max_length: 96
+    num_beams: 4
+    early_stopping: true
+datasets:
+- pszemraj/fleece2instructions-inputs-alpaca-cleaned
+language:
+- en
+pipeline_tag: text2text-generation
+library_name: transformers
+---
+
+
+# bart-large-instructiongen-w-inputs
+
+Use this text2text model to find out what LLM `instruction` (**and** `inputs` if relevant) might have generated `<arbitrary input text>`!
+
+This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the `pszemraj/fleece2instructions-inputs-alpaca-cleaned` dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.9302
+- Rouge1: 64.2236
+- Rouge2: 41.5632
+- Rougel: 60.5935
+- Rougelsum: 62.1285
+- Gen Len: 25.8938
+
+## example 
+
+![api](https://i.imgur.com/2xubG7N.png)
+
+## Intended uses & limitations
+
+This model is intended to be used to generate instructions from arbitrary text. You can then use these instructions + your data to fine-tune an LLM on instructions w.r.t. a specific domain. This model is primarily intended to enable **low-resource domain adaptation**, rather than "_I want to generate even better prompts for the FLAN-V2 dataset!_".
+
+The `fleece2instructions-inputs-alpaca-cleaned` dataset, obtained from the [alpaca-lora repo](https://github.com/tloen/alpaca-lora) under the ODC-BY license, has been converted to a text2text format for use with language models. In this dataset, the original 'inputs' and 'instructions' columns are combined into a single 'instructions_inputs' column. To clearly separate the two types of content, each piece of text is prefixed with either an `<instruction>` or `<inputs>` token. These tokens not only facilitate model comprehension, but also allow for easy regex separation of model outputs during inference. 
+
+As such, users can expect the output of this model to be similarly structured with `<instruction>` and `<inputs>` tokens.
+
+## Training and evaluation data
+
+Refer to the [fleece2instructions-inputs-alpaca-cleaned](https://huggingface.co/datasets/pszemraj/fleece2instructions-inputs-alpaca-cleaned) dataset
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 6e-05
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- distributed_type: multi-GPU
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.03
+- num_epochs: 3.0
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
+|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
+| 1.0145        | 1.0   | 1361 | 1.0460          | 62.8374 | 39.8538 | 59.2593 | 60.8095   | 25.2752 |
+| 0.8796        | 2.0   | 2722 | 0.9289          | 63.7086 | 41.1315 | 60.1588 | 61.7145   | 25.7215 |
+| 0.6943        | 3.0   | 4083 | 0.9302          | 64.2236 | 41.5632 | 60.5935 | 62.1285   | 25.8938 |

config.json

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+{
+  "_name_or_path": "pszemraj/bart-large-fleece2instructions-inputs-alpaca-cleaned-r1",
+  "activation_dropout": 0.1,
+  "activation_function": "gelu",
+  "add_bias_logits": false,
+  "add_final_layer_norm": false,
+  "architectures": [
+    "BartForConditionalGeneration"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": 0,
+  "classif_dropout": 0.1,
+  "classifier_dropout": 0.0,
+  "d_model": 1024,
+  "decoder_attention_heads": 16,
+  "decoder_ffn_dim": 4096,
+  "decoder_layerdrop": 0.0,
+  "decoder_layers": 12,
+  "decoder_start_token_id": 2,
+  "dropout": 0.1,
+  "early_stopping": true,
+  "encoder_attention_heads": 16,
+  "encoder_ffn_dim": 4096,
+  "encoder_layerdrop": 0.0,
+  "encoder_layers": 12,
+  "eos_token_id": 2,
+  "forced_bos_token_id": 0,
+  "forced_eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "init_std": 0.02,
+  "is_encoder_decoder": true,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
+  },
+  "max_position_embeddings": 1024,
+  "model_type": "bart",
+  "no_repeat_ngram_size": 3,
+  "normalize_before": false,
+  "num_beams": 4,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "scale_embedding": false,
+  "task_specific_params": {
+    "summarization": {
+      "length_penalty": 1.0,
+      "max_length": 128,
+      "min_length": 12,
+      "num_beams": 4
+    },
+    "summarization_cnn": {
+      "length_penalty": 2.0,
+      "max_length": 142,
+      "min_length": 56,
+      "num_beams": 4
+    },
+    "summarization_xsum": {
+      "length_penalty": 1.0,
+      "max_length": 62,
+      "min_length": 11,
+      "num_beams": 6
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.27.3",
+  "use_cache": true,
+  "vocab_size": 50265
+}

generation_config.json

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+  "decoder_start_token_id": 2,
+  "early_stopping": true,
+  "eos_token_id": 2,
+  "forced_bos_token_id": 0,
+  "forced_eos_token_id": 2,
+  "no_repeat_ngram_size": 3,
+  "num_beams": 4,
+  "pad_token_id": 1,
+  "transformers_version": "4.27.3"
+}

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special_tokens_map.json

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+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
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+    "single_word": false
+  },
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+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}

tokenizer_config.json

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+  "add_prefix_space": false,
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "mask_token": "<mask>",
+  "model_max_length": 1024,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "special_tokens_map_file": null,
+  "tokenizer_class": "BartTokenizer",
+  "trim_offsets": true,
+  "unk_token": "<unk>"
+}